Recommendation systems have two parts, generator and recommendation engine. The generator creates the recommendations. It lists N, usually 20, recommended items (or users) for each target item (user). It can be manual entry, but is usually an automated system using correlation, Bayesian, or neural networks to relate items based upon user actions. The recommendation engine receives requests for recommendations, and returns the recommended items. The request usually includes the number of desired recommendations and type of recommendations (cross-sell, similar, or personalized).
There are numerous examples in the prior art, including patent application Ser. No. 12/764,091 entitled “Improvements in Recommendations Systems” by Ken Levy and Neil Lofgren, and patent application Ser. No. 13/107,858 entitled “Further Improvements in Recommendation Systems” by Ken Levy and Neil Lofgren, both incorporated herein by reference. This application is a continuation-in-part of, and claims the benefit of, U.S. patent application Ser. No. 13/107,858 filed May 13, 2011 (published as US2011/0282821A1) entitled “Further Improvements in Recommendation Systems” which claims the benefit of Provisional Patent Application Ser. No. 61/334,185 filed May 13, 2010. This application is also a continuation-in-part of, and claims the benefit of, U.S. patent application Ser. No. 12/764,091 filed Apr. 20, 2010 (published as US2010/0268661A1) entitled “Improvements in Recommendation Systems” which claims the benefit of Provisional Patent Application Ser. No. 61/171,055 filed Apr. 20, 2009, Ser. No. 61/179,074 filed May 18, 2009, Ser. No. 61/224,914 filed Jul. 13, 2009, Ser. No. 61/229,617 filed Jul. 29, 2009, and Ser. No. 61/236,882 filed Aug. 26, 2009, all entitled “Improvements in Recommendation Systems”.
When working in social media, there are new problems, such as how to use social graphs to improve recommendations. Or, determine who will buy a product, especially a new product. It is not ideal to have your complete ecommerce store on a social media commerce site. In addition, if generating recommendations based upon the actions of friends, the system becomes extremely complex since the recommendations for each product have to be calculated for each user.
In addition, previously mentioned application Ser. No. 13/107,858 discusses using similarity measurements that are used in recommendations can be used to automate web analytics. The input is view data for URLs, and can include purchase data for products. These results can show which URLs, such as a video, lead to the purchase of a product (i.e. are related to the purchase of the product).
Furthermore, there are difficulties using recommendations in email, display boards, with new products, such as on flash sales sites, and using recently viewed items to dynamically update recommendations as a shopper browses a website.